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308 lines
8.9 KiB
Python
308 lines
8.9 KiB
Python
import cv2
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from tqdm import tqdm
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from ultralytics import YOLO
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from ultralytics.yolo.utils.plotting import Annotator
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import os
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import cv2
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from mediapipe.python.solutions import drawing_utils
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from mediapipe.python.solutions import hands
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import time
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import os
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import queue
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import threading
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from yolov8_det import analysis_yolov8
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from cut_img_bbox import cut_img_bbox
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from mediapipe_det import analysis_mediapipe
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class atm_det:
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def __init__(self,imgPath,savePath,modellist):
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self.imgPath = imgPath
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self.savePath = savePath
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self.imgList = os.listdir(self.imgPath)
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#定义加载好的模型
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self.model_person = modellist[0]
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self.model_pp_hand = modellist[1]
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self.model_blue = modellist[2]
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self.model_screen = modellist[3]
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self.media_hands = modellist[4]
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# 队列
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self.imgQueue1 = queue.Queue(maxsize=len(self.imgList))
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self.imgQueue2 = queue.Queue(maxsize=len(self.imgList))
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self.imgQueue3 = queue.Queue(maxsize=len(self.imgList))
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self.imgQueue4 = queue.Queue(maxsize=len(self.imgList))
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self.imgQueue5 = queue.Queue(maxsize=len(self.imgList))
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self.imgQueue6 = queue.Queue(maxsize=len(self.imgList))
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#线程
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self.get_imgThread = threading.Thread(target=self.get_img)
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self.get_person_resultThread = threading.Thread(target=self.get_person_result)
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self.get_hand_landmarkerThread = threading.Thread(target=self.get_hand_landmarker)
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self.get_blue_resultThread = threading.Thread(target=self.get_blue_result)
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self.get_pph_resultThread = threading.Thread(target=self.get_pph_result)
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self.analysis_handThread = threading.Thread(target=self.analysis_hand)
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self.draw_imagesThread = threading.Thread(target=self.draw_images)
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self.analysis_hand_blueThread = threading.Thread(target=self.analysis_hand_blue)
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self.get_screen_resultThread = threading.Thread(target=self.get_screen_result)
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def get_img(self):
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for img in self.imgList:
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imgpath = os.path.join(self.imgPath,img)
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images = cv2.imread(imgpath)
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imagesDict = {img:images}
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self.imgQueue1.put(imagesDict)
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def get_person_result(self):
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while True:
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if ~self.imgQueue1.empty():
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imagesDict = self.imgQueue1.get()
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images = list(imagesDict.values())[0]
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imgname = list(imagesDict.keys())[0]
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per_result = analysis_yolov8(images=images,
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model_coco=self.model_person,
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confidence=0.5
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)
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for per in per_result:
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per_bbox = list(per.values())[0]
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imgcut = cut_img_bbox(images,per_bbox)
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imgcutDict = {imgname:{"imgcut":imgcut,"per":per}}
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self.imgQueue2.put(imgcutDict)
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def get_blue_result(self):
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while True:
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if ~self.imgQueue1.empty():
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imagesDict = self.imgQueue1.get()
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images = list(imagesDict.values())[0]
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imgname = list(imagesDict.keys())[0]
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blue_result = analysis_yolov8(images=images,
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model_coco=self.model_blue,
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confidence=0.5
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)
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blues_list = []
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for blues in blue_result:
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blue = list(blues.values())[0]
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blues_list.append(blue)
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if blues_list:
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bluesDict = {imgname:blues_list}
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self.imgQueue4.put(bluesDict)
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def get_pph_result(self):
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while True:
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if ~self.imgQueue1.empty():
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imagesDict = self.imgQueue1.get()
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images = list(imagesDict.values())[0]
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imgname = list(imagesDict.keys())[0]
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blue_result = analysis_yolov8(images=images,
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model_coco=self.model_pp_hand,
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confidence=0.5
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)
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pph_list = []
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for blues in blue_result:
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blue = list(blues.values())[0]
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pph_list.append(blue)
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if pph_list:
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pphDict = {imgname:pph_list}
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self.imgQueue5.put(pphDict)
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def get_hand_landmarker(self):
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while True:
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if ~self.imgQueue2.empty():
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imgcutDict = self.imgQueue2.get()
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imgcut = list(imgcutDict.values())[0]["imgcut"]
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hand_landmarker_result = analysis_mediapipe(images=imgcut,
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hands=self.media_hands,
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parameter=hands.HAND_CONNECTIONS)
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handDict = {"hand_landmarker_result":hand_landmarker_result}
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list(imgcutDict.values())[0].update(handDict)
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self.imgQueue3.put(imgcutDict)
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def get_screen_result(self):
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while True:
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if ~self.imgQueue1.empty():
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imagesDict = self.imgQueue1.get()
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images = list(imagesDict.values())[0]
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imgname = list(imagesDict.keys())[0]
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screen_result = analysis_yolov8(images=images,
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model_coco=self.model_screen,
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confidence=0.5
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)
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print('screen_result:',screen_result)
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def analysis_hand(self):
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while True:
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if ~self.imgQueue3.empty():
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imgcutDict2 = self.imgQueue3.get()
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imgname = list(imgcutDict2.keys())[0]
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re_list = list(imgcutDict2.values())[0]
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pre_list = re_list['per']
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pre_list = list(pre_list.values())[0]
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# pre_x = int(pre_list[2] - pre_list[0])
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pre_x = int(pre_list[0])
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pre_y = int(pre_list[1])
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# pre_y = int(pre_list[3] - pre_list[1])
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hand_list = re_list['hand_landmarker_result']
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point_list = []
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for hand_point in hand_list:
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for point in hand_point:
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# print(point)
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point_x = int(point[0]) + pre_x
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point_y = int(point[1]) + pre_y
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point_list.append((point_x,point_y))
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if point_list:
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imgcutDict2.update({imgname:point_list})
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self.imgQueue6.put(imgcutDict2)
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def analysis_hand_blue(self):
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while True:
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if ~self.imgQueue4.empty() and ~self.imgQueue6.empty():
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blue_list = self.imgQueue4.get()
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hand_list = self.imgQueue6.get()
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print('blue_list:',blue_list)
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print('hand_list:',hand_list)
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while list(blue_list.keys())[0] == list(hand_list.keys())[0]:
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print(list(blue_list.keys())[0])
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def draw_images(self):
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while True:
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if ~self.imgQueue6.empty():
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img_hand_point = self.imgQueue6.get()
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imgname = list(img_hand_point.keys())[0]
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img = cv2.imread(os.path.join(self.imgPath,imgname))
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point_list = list(img_hand_point.values())[0]
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for point in point_list:
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cv2.circle(img, point, 1,(0,0,255), 2)
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cv2.imwrite(os.path.join(self.savePath,imgname),img)
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def run(self):
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self.get_imgThread.start()
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self.get_person_resultThread.start()
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self.get_hand_landmarkerThread.start()
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self.get_blue_resultThread.start()
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# self.get_pph_resultThread.start()
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self.analysis_handThread.start()
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# self.draw_imagesThread.start()
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self.analysis_hand_blueThread.start()
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self.get_screen_resultThread.start()
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if __name__ == '__main__':
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model_person = YOLO("model_files/bk1.pt")
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model_pp_hand = YOLO("model_files/best_pph.pt")
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model_blue = YOLO("model_files/best_butten.pt")
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model_screen = YOLO("model_files/best_screen.pt")
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media_hands = hands.Hands(
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static_image_mode=True,
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max_num_hands=4,
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min_detection_confidence=0.1,
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min_tracking_confidence=0.1)
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modelList = [model_person,model_pp_hand,model_blue,model_screen,media_hands]
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q = atm_det(imgPath='E:/BANK_XZ/data_file',
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savePath='E:/BANK_XZ/output_data',
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modellist=modelList)
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q.run()
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